Repetitive Control (RC) designed with state feedback that includes past error feedforward and current error feedback schemes for linear time-invariant systems is reintroduced. Periodic disturbances are common within repetitive systems and can be represented with a time-delay model. The proposed design focuses on isolating the disturbance model and finding the overall transfer function around the delay model. The use of the small gain theorem around the delay model assures disturbance accommodation if stability conditions are achieved. This paper reintroduces the designed RC controller within the state feedback in the presence of both past error and current error structures. Robustness conditions are investigated and set to enhance system performance in the presence of modelling mismatch, which represents the novel contribution in this paper. Simulations demonstrate the advantages of the robust conditions obtained while improving system performance for dynamic perturbations.

Selecting a proper initial input for Iterative Learning Control (ILC) algorithms has been shown to offer faster learning speed compared to the same theories if a system starts from blind. Iterative Learning Control is a control technique that uses previous successive projections to update the following execution/trial input such that a reference is followed to a high precision. In ILC, convergence of the error is generally highly dependent on the initial choice of input applied to the plant, thus a good choice of initial start would make learning faster and as a consequence the error tends to zero faster as well. Here in this paper, an upper limit to the initial choice construction for the input signal for trial 1 is set such that the system would not tend to respond aggressively due to the uncertainty that lies in high frequencies. The provided limit is found in term of singular values and simulation results obtained illustrate the theory behind.

Local priority hysteresis switching logic is associated with adaptive control convergence when using an infinite set of candidate parameters via constraints added to the switching scheme. In this paper, we reevaluate these constraints on the basis of the persistent excitation assumption. This makes room for the adaptive control to converge to its optimum, resulting in improved performance. Unconstrained local priority hysteresis switching logic is investigated, and global convergence conditions are proposed. This paper expands on the preliminary version of a conference paper [1] by adding numerical simulation examples to validate both the application and the advantage of the theory.

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Free-space optical (FSO) communication requires a line-of-sight connection between a transmitter and a receiver in which the information signal is modulated by an optical carrier that propagates in free space. The FSO channel is greatly affected by weather conditions such as fog, rain, and snow. In the literature, several adaptive techniques, such as power control (PC), have been suggested to mitigate channel link degradations. In this paper, we investigate the effects of snow and rain attenuation on the bit error rate (BER) of the FSO system using two types of modulations, the on-off keying (OOK) modulation and the pulse-position modulation (16-PPM). The effect of PC on the performance of FSO communications is also examined in this study. We evaluated the system's performance with two types of snow, wet snow and dry snow, as well as with different rain regions. Results show that PC improves the BER of the FSO system; a high rate of improvement is found for wet snow and rain. PC has almost no effect with dry snow because of the high attenuation and the limitations on transmitted power. The BER for 16-PPM is better than that for OOK modulation.